Spatio-temporal variations of cloud fraction based on circulation types in the Iberian Peninsula

2018 ◽  
Vol 39 (3) ◽  
pp. 1716-1732 ◽  
Author(s):  
D. Royé ◽  
N. Lorenzo ◽  
D. Rasilla ◽  
A. Martí
Keyword(s):  
Iberian Peninsula ◽  
Temporal Variations ◽  
Cloud Fraction ◽  
Circulation Types ◽  
Spatio Temporal

scholarly journals <i>p</i>CO<sub>2</sub> variability in the surface waters of the eastern Gulf of Cádiz (SW Iberian Peninsula)

Ocean Science ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 1225-1245 ◽  
Author(s):  
Dolores Jiménez-López ◽  
Ana Sierra ◽  
Teodora Ortega ◽  
Soledad Garrido ◽  
Nerea Hernández-Puyuelo ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Surface Waters ◽  
Temporal Variations ◽  
Mean Value ◽  
Gulf Of Cadiz ◽  
Partial Pressure Of Co2 ◽  
Gulf Of Cádiz ◽  
Eastern Shelf ◽  
Spatio Temporal ◽  
Guadalquivir River

Abstract. Spatio-temporal variations in the partial pressure of CO2 (pCO2) were studied during eight oceanographic cruises conducted between March 2014 and February 2016 in surface waters of the eastern shelf of the Gulf of Cádiz (SW Iberian Peninsula) between the Guadalquivir river and Cape Trafalgar. pCO2 presents a range of variation between 320.6 and 513.6 µatm with highest values during summer and autumn and lowest during spring and winter. For the whole study, pCO2 shows a linear dependence with temperature, and spatially there is a general decrease from coastal to offshore stations associated with continental inputs and an increase in the zones deeper than 400 m related to the influence of the eastward branch of the Azores Current. The study area acts as a source of CO2 to the atmosphere during summer and autumn and as a sink in spring and winter with a mean value for the study period of -0.18±1.32 mmol m−2 d−1. In the Guadalquivir and Sancti Petri transects, the CO2 fluxes decrease towards offshore, whereas in the Trafalgar transect fluxes increase due to the presence of an upwelling. The annual uptake capacity of CO2 in the Gulf of Cádiz is 4.1 Gg C yr−1.

Spatio-temporal variations of winter wheat water requirement and climatic causes in Huang-Huai-Hai Farming Region

2012 ◽  
Vol 20 (3) ◽  
pp. 356-362 ◽  
Author(s):  
Xiao-Lin YANG ◽  
Zhen-Wei SONG ◽  
Hong WANG ◽  
Quan-Hong SHI ◽  
Fu CHEN ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Temporal Variations ◽  
Water Requirement ◽  
Spatio Temporal

ASSESSMENT OF THE SPATIO-TEMPORAL VARIATIONS IN GROUND WATER STORAGE USING DOWN-SCALED GRACE DATA

2018 ◽  
Author(s):  
Hossein Sahour ◽  
◽  
Mohamed Sultan ◽  
Karem Abdelmohsen ◽  
Sita Karki ◽  
...  
Keyword(s):  
Ground Water ◽  
Water Storage ◽  
Temporal Variations ◽  
Grace Data ◽  
Spatio Temporal

scholarly journals A robust machine learning approach to SDG data segmentation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kassim S. Mwitondi ◽  
Isaac Munyakazi ◽  
Barnabas N. Gatsheni
Keyword(s):  
Temporal Variations ◽  
Data Repository ◽  
The Sustainable Development ◽  
Complex Interactions ◽  
Data Segmentation ◽  
Technological Advances ◽  
Machine Learning Approach ◽  
Development Goals ◽  
Data Source ◽  
Spatio Temporal

Abstract In the light of the recent technological advances in computing and data explosion, the complex interactions of the Sustainable Development Goals (SDG) present both a challenge and an opportunity to researchers and decision makers across fields and sectors. The deep and wide socio-economic, cultural and technological variations across the globe entail a unified understanding of the SDG project. The complexity of SDGs interactions and the dynamics through their indicators align naturally to technical and application specifics that require interdisciplinary solutions. We present a consilient approach to expounding triggers of SDG indicators. Illustrated through data segmentation, it is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources. The paper treats each SDG as a Big Data source node, with the potential to contribute towards a unified understanding of applications across the SDG spectrum. Data for five SDGs was extracted from the United Nations SDG indicators data repository and used to model spatio-temporal variations in search of robust and consilient scientific solutions. Based on a number of pre-determined assumptions on socio-economic and geo-political variations, the data is subjected to sequential analyses, exploring distributional behaviour, component extraction and clustering. All three methods exhibit pronounced variations across samples, with initial distributional and data segmentation patterns isolating South Africa from the remaining five countries. Data randomness is dealt with via a specially developed algorithm for sampling, measuring and assessing, based on repeated samples of different sizes. Results exhibit consistent variations across samples, based on socio-economic, cultural and geo-political variations entailing a unified understanding, across disciplines and sectors. The findings highlight novel paths towards attaining informative patterns for a unified understanding of the triggers of SDG indicators and open new paths to interdisciplinary research.

Spatio-temporal variations of sea surface halocarbon concentrations and fluxes from southern Yellow Sea

2014 ◽  
Vol 121 (2) ◽  
pp. 369-388 ◽  
Author(s):  
Gui-Peng Yang ◽  
Bin Yang ◽  
Xiao-Lan Lu ◽  
Hai-Bing Ding ◽  
Zhen He
Keyword(s):  
Yellow Sea ◽  
Temporal Variations ◽  
Sea Surface ◽  
Southern Yellow Sea ◽  
Spatio Temporal
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